This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.

Directorate for Biological Sciences through the National Science Foundation(ECCS-1128050)
; National Natural Science Foundation of China(61304079
; Fundamental Research Funds for the Central Universities(FRF-TP-15-056A3)
; State Key Laboratory of Management and Control for Complex Systems(20150104)
; Office of Naval Research(N00014-13-1-0562)
; Air Force Office of Scientific Research European Office of Aerospace Research and Development(13-3055)
; U.S. Army Research Office(W911NF-11-D-0001)
; China National Natural Science Foundation(61120106011)
; China Education Ministry Project 111(B08015)
; 61433004
; 61374105)